Comparible diagnostics for renewable energy power systems

ABSTRACT

A computer processor implemented method of measuring, monitoring, comparing and diagnosing the power generated of at least two renewable power systems provided to at least two consumers and alerting at least one of consumers in the event of comparative underperformance, the method taking into account at least two diagnostic variables including weather and the renewable power system cover status (such as covered by snow), wherein the at least one computing device determines comparative information for a predetermined geographic area based upon at least two diagnostic variables, and at least two normalized performances to provide a comparative value; and informing the consumer of the comparative value in the event of an underperforming comparative value.

This application claims priority to and is a continuation of co-pendingapplication Ser. No. 13/455,871 filed Apr. 25, 2012 entitled “ComparableDiagnostics for Renewable Energy Power Systems” which claims priority toand is a continuation of co-pending application Ser. No. 12/777,221filed May 10, 2010 (and now issued as U.S. Pat. No. 8,190,395) which isa continuation-in-part of co-pending application Ser. No. 11/949,035filed Dec. 2, 2007 (and now issued as U.S. Pat. No. 7,742,897) entitled“Systems and Methods for Monitoring and Diagnosing the Power Generatedby Renewable Power Systems” and claims priority to and is acontinuation-in-part of co-pending application Ser. No. 11/673,649 Feb.12, 2007 entitled “Systems and Methods for Providing Renewable PowerSystems by Aggregate Cost and Usage” all of which are incorporatedherein by reference.

The present invention relates to systems and methods for measuring anddiagnosing the power generated by renewable energy power systems. Morespecifically, methods and logic for solar system monitoring toaccurately account for specific weather conditions, e.g. snowyconditions, when computing diagnostic data that is based on comparingsolar systems.

As the field of renewable power/renewable energy systems grows,monitoring the performance and output of these systems on an ongoingbasis becomes more important for the following reasons: 1) it helpsquantify to the system owner the exact benefits of the system; 2) itinforms the system owner or service provider of issues so that they canbe resolved quickly, reducing wasted generation capacity; and 3) itprovides data which over time can be used to improve renewable energysystem design and performance.

Monitoring is becoming even more important as new solar technologies areintroduced to the market. Today, most installed Photovoltaic solararrays rely on poly silicon cells to generate electricity. These areencased in glass and are extremely durable, though expensive. Many newtechnologies will soon be on the market, some of which are printed onthin films and do not use silicon at all as a conductor. While thesetechnologies are much less expensive and will greatly aid in the spreadand adoption of solar power, their long-term efficiency and durabilityare less well known. Therefore, ongoing monitoring is critical to bothdeploying and supporting these systems.

Examples of monitoring of renewable energy systems may include whetherthe system is on or off, the amount of energy being generated, how thesystem is performing against expected performance and how the system isperforming given various environmental data. There are many factorswhich impact the generation from a solar renewable energy system,including shading, dust and dirt, wind/temperature, module degradationand inverter efficiency. Each factor can impact the system by over 10%,sometimes affecting output as much as 100%. It is fairly easy to seethat while each factor can impact the system, some are broadlyenvironmental and cannot be remedied (e.g. temperature), others arelocal and can be fixed relatively easily (e.g. dirt, shade or snow) andsome are system related and may need to be fixed immediately (e.g.inverter faults).

While all of these measurements are important, they do not always do asufficient job of quickly informing the system owner or service providerof a problem with the system. The reason they do an incomplete job isthat none provides an inexpensive and reliable means of 1) truly knowingthat a problem resides within a particular system (vs. being anenvironmental factor), and 2) diagnosing the magnitude of the problem.The reason for this is because many factors affect the performance ofsolar energy systems (both Photovoltaic and Solar Hot Water), and it isvery difficult to efficiently determine the specific issue remotely. Forexample, a system may be performing below expectation because ofexcessive wind or cloudiness, but this does not imply any fundamentalsystem issue and does not warrant a service visit. On the other hand,the system may be underperforming because of growth of nearby trees or athin film of dirt on the panels, which are easy to remediate by eitherthe owner or a service provider.

Another example of an important but potentially faulty environmentalmeasurement system is a locally placed sunlight metering device(radiance meter). If this produces readings which differ from theexpected output of the solar panels, it will highlight an issue. But ifboth are dirty due to the same ongoing dust from a local constructionsite, the issue will not be known.

It cannot be overstated how important it is that renewable energy solarsystems perform as close to optimally as possible. A system whichproduces electricity valued at $3,000 per year, which has a 10%degradation that can easily be fixed (e.g. through cleaning) loses $300per year. Given the 25+ year life of the system (and without accountingfor inflation in the value of electricity), this translates into a lossof $7,500 worth of generation, as well as the additional cost of the$7,500 (or more) the user must pay for power to replace the lostgeneration. An efficient solution to many of these potential losses iscomparative diagnostics, as opposed to, by way of example, potentiallycostly and complex sensors. While it is very difficult to isolate rootcauses of system issues (there are several different types and these canhave a significant impact as described above), by taking a statisticallysignificant sampling of comparable renewable energy systems within ageographic area, a Geographic Average can be created on an ongoingbasis. This will not control for every variable affecting each system(e.g. a system on a hill may experience more wind than a system in amore protected area), but given adjustments, comparing each system'sperformance against this point-in-time average can efficiently highlightissues with a given system and alert the system owner and serviceprovider that there is a potential issue that needs correction.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and claims.

SUMMARY OF THE INVENTION

The present invention relates generally to systems and methods formeasuring, monitoring, comparing and diagnosing the power generated byat least two renewable power systems provided to a consumer.

According to one embodiment of the present invention, a computerprocessor implemented method of measuring, monitoring, comparing anddiagnosing the power generated of at least two renewable power systemsprovided to at least two consumers and alerting at least one of theconsumers in the event of comparative underperformance, the methodcomprising the steps of; providing at least two renewable power systems,at least one computing device (which may be a data server), at least onegeneration monitoring device in communication with at least onerenewable power system and, optionally at least one communication nodein communication with at least one of the renewable power system,generation monitoring device and data server, determining at least twodiagnostic variables for each renewable power system and saving eachdiagnostic variable into at least one at least one computing device(which may be a data server), wherein at least two of the diagnosticvariables are weather and renewable power system cover status;determining the energy generated by each renewable power system for aspecific time period to provide a normalized performance and saving thenormalized performance in at least one at least one computing device(which may be a data server) along with the corresponding weather andrenewable power system cover status; wherein the at least one at leastone computing device (which may be a data server) determines comparativeinformation for a predetermined geographic area based upon at least twodiagnostic variables, and at least two of the normalized performances toprovide a comparative value; and informing the consumer of thecomparative value in the event of an underperforming comparative value.

According to another embodiment of the present invention, a computerprocessor implemented method of measuring, monitoring, comparing anddiagnosing the power generated of at least two renewable power systemsprovided to at least two consumers and alerting at least one of theconsumers in the event of comparative underperformance taking intoaccount the effects of power system cover status (e.g. snow coveringsolar panels, debris from inclement weather covering solar panels,etc.), the method comprising the steps of; providing at least tworenewable power systems, at least one computing device (which may be adata server), at least one generation monitoring device in communicationwith at least one renewable power system and optionally at least onecommunication node in communication with at least one of renewable powersystem, generation monitoring device and at least one computing device(which may be a data server), determining at least two diagnosticvariables for each renewable power system and saving each diagnosticvariable into at least one data server, wherein at least two of thediagnostic variables are weather and renewable power system coverstatus; selecting a predetermined temperature threshold for a geographicarea and issuing a directive to the communication node to either obtaindiagnostic data or ignore diagnostic data from a particular renewablepower system according to whether the temperature is above or below apredetermined temperature threshold; issuing a directive to thecommunication node to either obtain diagnostic data or ignore diagnosticdata according to the renewable power system cover status; determiningthe energy generated by each renewable power system which were issued adirective to the communication node to obtain diagnostic data for aspecific time period to provide a normalized performance and saving thenormalized performance in at least one computing device (which may be adata server) along with corresponding weather and renewable power systemcover status; wherein at least one computing device (which may be a dataserver) determines comparative information for a predeterminedgeographic area based upon at least two of diagnostic variables, and atleast two of normalized performances to provide a comparative value; andinforming the consumer of the comparative value in the event of anunderperforming comparative value.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the present invention;

FIG. 2 depicts the present invention;

FIG. 3 depicts the present invention;

FIG. 4 depicts the present invention;

FIG. 5 depicts the present invention; and

FIG. 6 depicts the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description is of the best currently contemplatedmodes of carrying out the invention. The description is not to be takenin a limiting sense, but is made merely for the purpose of illustratingthe general principles of the invention, since the scope of theinvention is best defined by the appended claims.

The present invention provides a method, software applications andformulas to gather data on different renewable energy systems within ageographic area, and to use this data from the different systems toefficiently derive important Comparative Diagnostic information on eachof the systems on an ongoing basis. This diagnostic information can beused to detect and report system issues. Specifically, the Inventiondoes this by assigning individual System Coefficients to each renewableenergy system (for example, a solar photovoltaic system) when they areset up. These System Coefficients contain important characteristics suchas expected generation for the system, expected sunlight for thespecific location, impacting features such as roof pitch, buildingorientation, etc. Then, on an ongoing basis, the Invention utilizes adata gathering/processing infrastructure to calculate individualnormalized performances for each renewable energy system, and thencombines these to create a statistically significant geographic averagefor each local area. Each individual system's normalized performance isthen compared over set durations against the geographic average for thearea. Since these geographic averages inherently control for complexenvironmental data, they provide accurate renewable energy systemdiagnostics with less expense and fewer required sensors. In addition,the present invention provides a method and a system for monitoring thediagnostics, and providing them to both the owner of the system as wellas third parties who are then able to address the issues with therenewable energy systems. For example, service providers who may correctissues with the renewable energy system. The term renewable power systemmay be interchangeable with at premise renewable power system, renewableenergy system and at premise renewable energy system. The renewablepower system may be, by way of example, a solar system, solar panelsystem, photovoltaic, thermal, wind powered, geothermal, hydropower.

With reference to FIGS. 1-6, a computer processor implemented (e.g.server 10) method of measuring, monitoring and comparing the powergenerated of at least two renewable power systems (e.g. 102, 104, 106,108, 110, 112) provided to a consumer is provided. According to oneembodiment, to be statically relevant at least forty (40) renewablepower systems must be monitored and data acquired from the renewablepower systems. There may be at least two renewable power systems (e.g.102, 104, 106, 108, 110, 112), at least one data server (10), at leastone generation monitoring device (16) in communication with one atpremise renewable power system (30) and at least one communication node(22) in communication with at least one of the renewable power system(30), the generation monitoring device (16), the data server (10). Therenewable power system may have Background Constants that are enteredinto the system during data setup; populating this part of the datastructure is one of the initial steps to the process. During this time,all required (or potentially required) background information is loadedinto the system. This data will later be used for system calculationsand diagnostics. Background constants may include: (1) full calendarwith sunrise and sunset according to latitude throughout the year; (2)insolation or ‘incident solar radiation’: This is the actual amount ofsunlight falling on a specific geographical location. There are expectedamounts of radiation which will fall on an area each day, as well as anannual figure. Specific insolation is calculated as kWh/m2/day. Theimportance of this variable is that it can combine several otherbackground constants; and (3) location functionality. This softwarehelps determine the proximity of each system to each other system, andforms a key part of the algorithm used to determine the geographicaverage of the renewable energy systems. While there are differentspecific methods of implementing location functionality, generally thisrelies on a large database of locations which are tied to zones. Becausethe relational distances between the zones are stored within thesoftware, the distances between any two locations can then be easily andaccurately calculated.

There may be the step of determining at least one diagnostic variablefor each renewable power system (30). These are the variables whichaffect the diagnostics. Examples include: (1) adjustments to thepermitted size of each zone. When there are fewer systems within anarea, the algorithm may be adjusted to allow for a statisticallyaccurate comparison, but this would also reduce the ability of thecomparison to control as well for weather, etc., since there may be morevariances over the distance; (2) adjustments to the sensitivity of thediagnostics. Changing thresholds will impact when a particular renewableenergy system is identified by the software as having an issue. Each atleast one diagnostic variable is saved into at least one at least onecomputing device (which may be a data server).

The method may comprise the step of determining at least one systemcoefficient for each renewable power system and saving each systemcoefficient in the data server(s). These are set up when each newrenewable energy system is entered into the software, and they providekey information about the expected performance of each system. Thecoefficients will generally stay the same for the life of the system.Not all system coefficients will be used in each comparative diagnostic,but they all may be used at some point to provide additionalfunctionality to the process. One critical aspect of the systemcoefficients is that ALL coefficients for all renewable energy systemsmust be assigned using the same approach and rules. The reason for thisis that if the system coefficients are not uniformly applied, thenormalized performance values (described below) will not be accurate.The items which can make up the system coefficient include: (1) expectedgeneration by day, month and year for the system; (2) expectedgeneration on an ongoing basis (e.g. average generation each day,regardless of time of the year); (3) renewable energy system size; (4)system technology; (5) system tolerances (e.g. how accurate informationfrom the system's components will be); (6) system shading; (7) systemorientation; and (8) an ‘adjustment factor’ which can be entered byadmin to adjust for how the system actual works to reduce ‘false’signals if the system does not work as initially expected. This isrepresented as a percentage affecting the calculations:

The energy generated by each renewable power system is recorded and theat least one computing device (which may be a data server) thendetermines comparative information based upon at least one of thebackground constant, the diagnostic variable, the system coefficient andthe energy generated to determine a comparative value of the renewablepower system. The term comparative value is intended to include anyvalue that compares one system to another system or a group of systems.For example, this may be as simple as an “underperforming” designationwhen the system's performance is less than another system or group ofsystems performance in terms of power generated.

The normalized performance is then calculated. This is an ongoingcalculation which is done for each renewable energy system which isconnected to the software. Essentially, the normalized performance is arenewable energy system's actual performance for a given time periodmultiplied by its individual system coefficient.

The formula for this for a given time period is: NP=Gen*SC

This equation is fundamental to performing comparative information andcomparative diagnostics since it enables the results from disparatesystems to be ‘normalized’ and therefore directly compared. Thesenormalized performance readings can then aggregated into larger periods(24 hours, 7 days, 1 month) for comparison.

The comparative information may be a ‘geographic average’. Thegeographic average may be calculated by an algorithm which produces anaverage of the normalized performances for each area covered. Thepurpose is to be able to efficiently perform comparative diagnosticsregularly (e.g. every 5 minutes) for a large number of areas containinga large number of renewable energy systems without overly taxing theservers' processors. The main steps of this algorithm are as follows:(1) The software will randomly select forty (40) renewable energysystems within each of the algorithm's defined localities; (2) Thenormalized performance for these forty (40) systems will be summed andthen divided by forty (40); (3) This results in a statisticallysignificant geographic average which will then be recorded on thedatabase; (4) An important feature of this algorithm is that much of thecomplexity is dealt with at the individual renewable energy system levelwith the system coefficient. The simplicity of the algorithm isimportant for processor efficiency and ongoing data production.

There may be a comparative diagnostics process. Once the normalizedperformance and the geographic average are known for a given renewableenergy system, these two figures can be compared to determine how wellthe system is performing vis-à-vis expectations. The reason the resultsof this are valid regardless of time of year or environmental conditionsis because these factors are all controlled for within the localsampling of related systems. Based on the factors noted above which candegrade system performance, the comparative diagnostics can be used todetermine when a renewable energy system is performing sub optimally,therefore the comparative value may be “underperforming” or any otherterm indicating an issue. If the comparative value falls below certainthresholds (e.g. 7.5% below the geographic average signals a systemissue) these diagnostics can then be used to remediate the issue andimprove the system's performance. The diagnostics can be broken out intoperiods of different length (24 hours, 7 days and 1 month) which havethe following benefits: (i) 24 Hour Comparisons: While a 5 minutesegment may show variance between one renewable energy system and thegroup's geographic average, the variance may not be due to any inherentsystem issue (e.g. a single cloud may be temporarily affecting onesystem without impacting others in the vicinity). However, over a 24hour period, these environmental factors are normally controlled for,and any significant variance will reveal a legitimate system issue. Thefact that this is done over a rolling 24 hours means that theinformation will be received by the renewable system owner or serviceprovider relatively quickly, without the requirement of waiting for aweekly or monthly report; (ii) weekly comparisons: though this does notprovide the same sort of timely information that is provided by 24 HourComparisons, the additional data will allow for more accurate diagnosticcomparisons since environmental variables will have even less impact;(iii) monthly comparisons: While more accurate than weekly comparison,this will be used mostly as a reporting mechanism to show system statusand performance.

There may be at least one inverter (14) in communication with therenewable power system (e.g. 50, 30). The inverter (14) is an electroniccircuit that converts direct current (DC) to alternating current (AC).There may also be at least one return monitor (18) determining theenergy returned to a grid by said at-least one renewable power system.The method may comprise the step of determining at least one backgroundconstant and saving each background constant in a computing device,which may be a computing device such as a data server(s). Note that theterm computing device may be any computing device or network ofcomputing devices. By way of example a computing device may be acomputer, data server, ipad®, tablet device, mobile device, smart phoneor any other computing device as would be appreciated by one of ordinaryskill in the art.

There may also be the steps of determining if the renewable power systemcan be remotely adjusted and remotely making a change to the renewablepower system. By way of example, it may be possible to remotely monitorthe system and change the angle of a solar panel to provide increasedsunlight.

The system for measuring, monitoring, comparing and diagnosing the powergenerated from at least two renewable power systems as it is generatedat a consumer's premises may have: at least two renewable power systems,wherein each renewable power system (e.g. 30, 50) is at least partiallypowered by at least one alternate energy source; at least one generationmonitoring device (e.g. 58), wherein the at least one generationmonitoring device (e.g. 58) calculates the energy generated at eachconsumer's premises by the renewable power system (e.g. 30, 50); atleast one communication node (64) in communication with each at leastone generation monitoring device (e.g. 58); at least one computingdevice such as a data server (10) in communication with communicationnode (e.g. 64), wherein the at least one computing device (which may bea data server 10) accepts information from the communication node (e.g.64) to determine the power generated at a first user's premises (100)and compare the power generated at a first user's premises (100) toComparative Information obtained from at least two renewable powersystems (e.g. 102, 104, 106, 108, 110, 112, 114) to determine if thefirst user's renewable power system (100) is within a predetermineddeviation from the comparative Information. This may provide acomparative value. The communication node may be further comprising adata storage means for storing usage information. The generationmonitoring device may be selected from the group consisting of pulsemeter, temperature meter, electromechanical meter, solid state meter,flow meter, electric meter, energy meter and watt meter. There may alsobe at least one return monitoring device in communication with theinverter which calculates the energy returned to a grid by the system.

The at-premise renewable power system may be, for example, a solarsystem, solar panel system, photovoltaic, thermal, wind powered,geothermal, hydropower. A secondary energy source (e.g. 52) may be incommunication with and at least partially powering the renewable powersystem. It should be understood, though, this is only for ancillarypower in the event that the renewable energy source (50) is not capableof entirely powering the at premise renewable power system.

The generation monitoring device may be any type of meter, by way ofexample, this may include a pulse meter, temperature meter,electromechanical meter, solid state meter, flow meter, electric meter,energy meter and watt meter. Each installation of the Invention willhave a communication node or hub set up at the location with the system.One of the communication nodes may act as a hub. These devices connectto the internet and send the data collected by the nodes to the Server.They have the following properties: The hub has a web server andconnects to a standard internet connection (Ethernet). It does notrequire a computer or other device to make this connection. Each hub hasits own unique IP or DNS address. The hub is configured by a webbrowser. The web browser allows the hub to have specific nodes assignedto it. This set up feature will allow another hub in the area to be setup with its own nodes so that all can operate wirelessly withoutdisruption. Also, the hub is able to configure specific aspects of thehub, such as the connection with the server, data recording and timesettings and the ability to configure the attached nodes, includingtheir recording properties.

The hub may connect wirelessly or through wire-line including through ACpower to the various nodes in its network and may handle several nodes,sending up the data of each within a separate data stream to the server.Typically the hub would plug into a standard AC outlet and have LEDs toshow operation and diagnostics. The hub may also record data, so that ifthe Internet connection is ever down, data from the nodes will not belost. It will also have the option of a tamper resistant casing andminor router capabilities—so that it could stand in front of a standardrouter and act as the primary data entry point for a location. The hubwill also be able to operate behind a router. All of the hubs mayconnect to a centralized database for data aggregation. This databasewill be able to relate the information from each node according to thetime recorded. Specific data which will be saved may include: (1) hubIP#/DNS information; (2) node IP#/DNS information/name; (3) timestampincrement; (4) hot water flow by unit (gallon or liter) per timeincrement; (5) electric flow by unit (kilowatts) per time increment; (6)fuel flow by unit (depends on fuel type) per time increment; and (7)other information as required (e.g. water temperature).

Each installation of the Invention will have typically one or moregeneration recording nodes. These are typically connected wirelessly tothe hub, and connected directly to the inputs/outputs from the renewablepower system. They communicate constantly with the various devices andtransfer data which is then sent to the server. They may have thefollowing properties: The first required node connects to a flow meterattached to the water tank that is connected to the solar hot watersystem. This node will operate as a pulse meter, ‘clicking’ whenever aunit (either a gallon or a liter) of hot water passes from the tank. Thesecond required node connects to either the electric panel at the switchfor the Hot Water tank's electric power OR to a flow/other meter forgas/oil to the secondary heater for the Hot Water tank. The node mayhave a data storage means for storing flow/usage information. There mayalso be other nodes, which may be used to measure other aspects of thesystem and gain even more accurate readings. For example: thetemperature of the hot water on an ongoing basis. The system may bemonitored from a remote location (such as a computer in a differentlocation).

The present invention provides monitoring for solar systems, computesdiagnostics by comparing systems and benefits from being able to tellwhen a particular system is experiencing snowy conditions. This presentinvention provides methods and system so that it can be correctlydetermined when a solar panel is being snowed on and when it is coveredor partially covered by snow. If a panel is currently being snowed on orhas been snowed on, it may be excluded from the statistical pool. In theabsence of this, it cannot be differentiated between snowy and non-snowyconditions, which can result in false-negatives/false-positives whendiagnostics are computed. This can typically occur the following dayafter a snow storm. Due to varying angles of incidence in each solarpanel one system may have completely shed it's snow coverage whileanother may still be completely covered. In this case, diagnostics thatdo not account for snow will report both falsely that the panel withoutcoverage is outperforming expectations and the one covered isexperiencing a critical issue. The Methodology seeks to remedy this andsimilar situations by identifying snow covered panels and to excludethem from radial area comparisons. This will provide accurate reportingand allow a normal diagnostic for non-covered panels and accuratelyalert the covered panel's user or service provider.

According to one aspect of the present invention, a computer processorimplemented method of measuring, monitoring, comparing and diagnosingthe power generated of at least two renewable power systems (e.g. 102,104, 106, 108, 110, 112) provided to at least two consumers and alertingat least one of said consumers in the event of comparativeunderperformance is provided, the method comprising the steps of;providing at least two renewable power systems (e.g. 102, 104, 106, 108,110, 111, 112), at least one computing device such as a data server(10), at least one generation monitoring device (16) in communicationwith at least one said renewable power system and at least onecommunication node (22) in communication with at least one of therenewable power system, the generation monitoring device and thecomputing device such as a data server, determining at least twodiagnostic variables for each renewable power system and saving eachdiagnostic variable into the at least one computing device, wherein atleast two of the diagnostic variables are weather and renewable powersystem cover status. The weather and temperature may be obtained from athird party internet (300) weather feed source and grouped by zip codeand saved in at least one computing device which may be a data server(10). The weather may also be obtained from a third party internetweather feed source and the temperature is obtained by a temperatureprobe (302) attached to the renewable power system and the weather andtemperature are grouped by zip code and saved in at least one computingdevice which may be a data server (10). Each time a solar system'sdiagnostic is computed, it's location may be matched to a particularweather and temperature based on the system's zip code. If a systemmanager chooses, he can attach a temperature probe to the solar panelsto get a more accurate temperature than the one provided by the weatherfeed. The method may next determine the energy generated by eachrenewable power system for a specific time period to provide anormalized performance and save the normalized performance in at leastone computing device which may be a data server (10) along with thecorresponding weather and renewable power system cover status. Therenewable power system cover status may be covered, partially covered,uncovered and indeterminate. For example, when it has snowed, or therehad been a volcanic eruption covering the roof with a layer of soot, therenewable power system cover status would be covered. The roof may alsobe partially covered (for example, there may be unmelted snow on oneportion of the roof, or debris covering a portion of the roof). The roofwould be uncovered in its normal state and it would be indeterminate inmany cases including a communication error with a particular renewablepower system. The preferred renewable power system in the presentinvention would be solar panels, but it could be any renewable powersystem and the likely issue presented with the panels being covered,provided herein without limitation, would be snow covering or partiallycovering the panels. However, there are many issues that could presentcovered solar panels including debris, a dust storm, volcanic ash, etc.During the time when the diagnostic data is computed, if the softwarelogic finds that the current weather conditions is “snowing” then itwill immediately cease calculating. It will instead mark a fieldindicating “is snowing at this time” within the software to record thefact that it was snowing at the time. There may be the issue of no orlow production following an event, such as snow precipitation orcoverage. The at least one at least one computing device which may be adata server (10) determines comparative information for a predeterminedgeographic area based upon at least two of the diagnostic variables, andat least two of the normalized performances to provide a comparativevalue ; and informs the consumer of the comparative value in the eventof an underperforming comparative value.

During diagnostic computation, if a system was set “is snowing at thistime,” “snow is covering panel” (resulting in a renewable power systemcover status of COVERED)” or “snow is partially covering panel”(resulting in a renewable power system cover status of PARTIALLYCOVERED), the method will look at the energy production. The presentinvention providing a threshold time period and determining the energygenerated by each renewable power system for the threshold time period,then sending an alert to the consumer according to the energy generatedby each renewable power system for the threshold time period. By way ofdescription, without limitation, if no energy was produced in the lasttime period and the environmental temperature is below a threshold, acountdown timer is set. If no energy was produced, and the environmentaltemperature is above a threshold, the countdown timer decremented. Ifthe timer falls below 0, an alert is sent to the queue indicating anissue. If some energy was produced in the last time period, thediagnostic production value is below expectations, and the environmentaltemperature was below a threshold, a countdown timer is set. If insteadthe environmental temperature was above a threshold, the countdown timerdecremented. If the timer falls below 0, an alert is sent to the queueindicating an issue. Once all systems are checked for potential snowcoverage issues individually (i.e., step 5), a check is made for outliersystems in a given area. If less than X % (a predetermined percentage,for example five percent (5%)) of systems in a given area are affectedby snow, flag the outlier systems that were affected (e.g., if only 1out of 1000 systems in an area are affected, there is a good chance theone system tagged as affected by snow actually has a different type ofproblem). If Y % of systems in a given area that were affected by snowhave since recovered, flag the systems that have not recovered aspotentially having an issue (e.g., if only 1 system out of 1000 affectedhas not recovered, it may actually have a different issue).

Each system user or service provider may set a temperature threshold.The threshold should be set to a degree that regardless of thatparticular system's panel's angle of incidence, if the averagetemperature over the hour was above the threshold there is nopossibility of snow covering the panel (for example, it may be selectedto be 32 degrees Fahrenheit or 40 degrees Fahrenheit. If the temperaturefeed stated above is available, the average temperature is computedusing this data, otherwise the weather feed information is used. Whenthe software is computing the diagnostic for any given system it shouldexclude nearby systems that fall in it's radial area if any of thespecial snow fields are set to true. The special snow fields are “issnowing at this time,” “snow is covering panel (renewable power systemcover status of COVERED),” and “snow is partially covering panel”(renewable power system cover status of PARTIALLY COVERED). Therenewable power system cover status may be determined by the weatherstatus (for example SNOWING) or the comparative diagnostics (forexample, if the power generated by a geographic area is significantlyreduced, we know it is not a single renewable power system, accordingly,we do not alert the consumer, as it is a temporary issue and not onethat requires alerting the panel's user or service provider. As shown inFIG. 5, systems 111 are snow COVERED, accordingly they may be ignoredfrom geographic comparisons. There may also be the step of determining ageographic average of at least two renewable power systems; determiningthe energy generated by each renewable power system for a specific timeperiod and saving the energy generated in the at least one at least onecomputing device which may be a data server (10) along with thecorresponding renewable power system cover status. Then the at least onecomputing device (which may be a data server (10)) determinescomparative information for the predetermined geographic area and atleast two of the normalized performances to provide a comparative value;and informs the consumer of the comparative value in the event of anunderperforming comparative value. There may be the situation that eventhough an entire geographic area is at experiencing a partially coveredrenewable power system cover status, a single system is stillunderperforming. In this case, it may be indicative of a problem andwould inform the consumer.

There may also be the step of selecting a predetermined temperaturethreshold for a geographic area and issuing a directive to thecommunication node to either obtain diagnostic data or ignore diagnosticdata from a particular renewable power system according to whether thetemperature is above or below the predetermined temperature threshold.For example, if the temperature is above 50 degrees Fahrenheit, we knowa snow event resulting in a renewable power system cover status ofCOVERED is highly unlikely. The present invention also provides that adirective may be issued to the communication node to either obtaindiagnostic data or ignore diagnostic data according to the renewablepower system cover status. For example, if it determined that therenewable power system cover status is COVERED, we may want to ignorediagnostic data.

There may also be the steps of randomly selecting a predetermined numberof renewable power systems in a predetermined geographic area;determining the normalized performance of each randomly selectedrenewable power system, summing the normalized performances to provide asum and dividing the sum by the predetermined number to provide ageographic average performance; and saving the geographic averageperformance in said at least one computing device which may be a dataserver (10). The consumer may be alerted if their at least one renewablepower systems performance is a predetermined amount lower than thegeographic average performance.

The present invention also envisions that it may be determined if the atleast one renewable power system can be remotely adjusted; and if it canbe remotely adjusted, there present invention performs the step ofremotely making a change to the at least one renewable power system. Forexample, if a solar panels is partially covered and the system may beremotely adjusted up and down, a slight shift may knock the snow off,allowing the solar panel to be uncovered and fully operational.

By way of example, the following provides a snow condition algorithmthat may be stored in the at least one computing device which may be adata server (10) as in the present invention.

Definition of Variables: SID = Unique System Identifier Wf = CurrentWeather Condition avgT = Average Temperature (over last 24 hours)threshT = User Defined Temperature Threshold Sn = “Is Snowing At ThisTime” Field Sc = “Snow Is Covering Panel” Field Spc = “Snow Is PartiallyCovering Panel” Field DV = Diagnostic Value newDV = New Diagnostic ValuethreshDV = Diagnostic Threshold EN = Energy Produced by System NSI =Nearby System Indicator Timer = countdown timer to flag malfunctioningsystems AffectedSystemThreshold = % threshold for flagging outliersystems affected by snow AreaRecoveredThreshold = % threshold foroutlier systems not recovering from snow coverage f_AreaAffectedSystems= % of systems in Sn, Sc, Spc states in a particular areaf_AreaRecoveredSystems = % of systems in a particular area that changedfrom Sn, Sc, or Spc states TRUE to FALSE in the last time periodAlgorithm-Individual system DIAGNOSTIC_COMPUTATION { FOR (EACH SYSTEM)f_GET_WEATHER_INFO_BY_ZIP (SID) { RETURNS (Wf,avgT) } IF (Wf equals“Snowing”) SET Sn = TRUE, Sc = FALSE, Spc = FALSE, DV = NULL ELSE IF(current time is daytime) f_GET_ENERGY_PRODUCED_LAST_HR (SID) { RETURNS(EN) } f_GET_SNOW_DATA_FROM_LAST_HR (SID) { RETURNS(Sn, Sc, Spc) } IF(EN<= 0) IF(Sn equals TRUE OR Sc equals TRUE OR Spc equals TRUE) IF (avgT <threshT) Timer = default f_ADD_TO_ALERT_QUEUE (SID) { “MILD - Panel IsCovered” } SET Sn = FALSE, Sc = TRUE, Spc = FALSE, DV = NULL ELSE IFTimer = Timer − 1 IF (Timer < 0) f_ADD_TO_ALERT_QUEUE (SID) {“CRITICAL - Issue With Panel” } SET Sn = FALSE, Sc = FALSE, Spc = FALSE,DV = NULL ELSE f_COMPUTE_DIAGNOSTIC_FOR_SYSTEM (SID)† { RETURNS(newDV) }SET Sn = FALSE, Sc = FALSE, Spc = FALSE, DV = newDV ELSEf_COMPUTE_DIAGNOSTIC_FOR_SYSTEM (SID)† { RETURNS(newDV) } IF(newDV <threshDV) IF(Sn equals TRUE or Sc equals TRUE or Spc equals TRUE) IF(avgT < threshT) Timer = default f_ADD_TO_ALERT_QUEUE (SID) { “MILD -Panel Is Partially Covered” } SET Sn = FALSE, Sc = FALSE, Spc = TRUE, DV= newDV ELSE Timer = Timer − 1 IF(Timer < 0) f_ADD_TO_ALERT_QUEUE (SID){ “CRITICAL - Issue With Panel” } SET Sn = FALSE, Sc = FALSE, Spc =FALSE, DV =NULL ELSE SET Sn = FALSE, Sc = FALSE, Spc = FALSE, DV = newDV} †- Note: This Function Ignores All Nearby Systems That Have Sn, Sc,Spc Equal to True Algorithm-comparison of snow-affected systemsDIAGNOSTIC_COMPUTATION { FOR (EACH AREA) { IF (f_AreaAffectedSystems <AffectedSystemThreshold) f_ADD_TO_ALERT_QUEUE (SID) { “MILD - Panelappears affected by snow when neighbors are unaffected” } IF(f_AreaRecoveredSystems < AreaRecoveredThreshold) f_ADD_TO_ALERT_QUEUE(SID) { “MILD - Panel has not recovered from snowfall, despite mostneighbors recovering” } } }

It should be understood that the foregoing relates to preferredembodiments of the invention and that modifications may be made withoutdeparting from the spirit and scope of the invention as set forth in thefollowing claims.

I claim:
 1. A computer processor implemented method of measuring,monitoring, comparing and diagnosing the power generated of at least tworenewable power systems provided to at least two consumers and alertingat least one of said consumers in the event of comparativeunderperformance, said method comprising the steps of; providing atleast two renewable power systems, wherein each said renewable powersystem is assigned a system coefficient and is in communication with atleast one computing device, determining at least two diagnosticvariables for each said renewable power system and saving each saiddiagnostic variable into said at least one computing device; determiningthe energy generated by each said renewable power system for a specifictime period to provide said renewable power system's energy generatedperformance for a given time period; determining, by said at least onecomputing device, the normalized performance for each said renewablepower system, wherein the normalized performance is said renewable powersystem's energy generated performance for a given time period multipliedby said system coefficient to provide a normalized performance; savingsaid normalized performance and said diagnostic variables in said atleast one computing device; determining, by said at least one computingdevice, comparative information for a predetermined geographic areabased upon at least two of said diagnostic variables and at least two ofsaid normalized performances to provide a comparative value; andinforming said consumer of said comparative value in the event of anunderperforming comparative value.
 2. A computer processor implementedmethod as in claim 1, wherein at least two of said diagnostic variablesare weather and renewable power system cover status.
 3. A computerprocessor implemented method as in claim 2, wherein said correspondingrenewable power system cover status is selected from the groupconsisting of covered, partially covered, uncovered and indeterminate.4. A computer processor implemented method as in claim 2, wherein saidweather and temperature are obtained from a third party internet weatherfeed source and grouped by zip code and saved in said at least onecomputing device.
 5. A computer processor implemented method as in claim2, wherein said weather is obtained from a third party internet weatherfeed source and said temperature is obtained by a temperature probeattached to said renewable power system and said weather and temperatureare grouped by zip code and saved in said at least one computing device.6. A computer processor implemented method as in claim 2, furthercomprising the step of: determining a geographic average of at least tworenewable power systems; determining the energy generated by each saidrenewable power system for a specific time period and saving said energygenerated in said at least one computing device along with saidcorresponding renewable power system cover status
 7. A computerprocessor implemented method as in claim 1, further comprising the stepof: selecting a predetermined temperature threshold for a geographicarea and issuing a directive to either obtain diagnostic data or ignorediagnostic data from a particular renewable power system according towhether the temperature is above or below said predetermined temperaturethreshold.
 8. A computer processor implemented method as in claim 2,further comprising the step of: issuing a directive to either obtaindiagnostic data or ignore diagnostic data according to said renewablepower system cover status.
 9. A computer processor implemented method asin claim 8, further comprising the step of: providing a threshold timeperiod and determining the energy generated by each said renewable powersystem for said threshold time period; sending an alert to said consumeraccording to said energy generated by each said renewable power systemfor said threshold time period.
 10. A method as in claim 1, wherein saidstep of determining comparative information for a predeterminedgeographic area based upon at least two of said diagnostic variables,and said normalized performance to provide a comparative value isfurther comprising the step of: randomly selecting a predeterminednumber of renewable power systems in said predetermined geographic area;determining the normalized performance of each said randomly selectedrenewable power system, summing said normalized performances to providea sum and dividing said sum by said predetermined number to provide ageographic average performance; and saving said geographic averageperformance in said computing device.
 11. A method as in claim 10,further comprising the step of: alerting at least one consumer when saidat least one renewable power systems performance is a predeterminedamount lower than said geographic average performance.
 12. A method asin claim 1, further comprising the steps of: determining if said atleast one renewable power system can be remotely adjusted; remotelymaking a change to said at least one renewable power system.
 13. Acomputer processor implemented method of measuring, monitoring,comparing and diagnosing the power generated of at least two renewablepower systems provided to at least two consumers and alerting at leastone of said consumers in the event of comparative underperformance, saidmethod comprising the steps of; providing at least two renewable powersystems, wherein each said renewable power system is assigned a systemcoefficient and is in communication with at least one computing device;determining at least two diagnostic variables for each said renewablepower system and saving each said diagnostic variable into at least onecomputing device, wherein at least two of said diagnostic variables areweather and renewable power system cover status; selecting apredetermined temperature threshold for a geographic area and issuing adirective to either obtain diagnostic data or ignore diagnostic datafrom a particular renewable power system according to whether thetemperature is above or below said predetermined temperature threshold;issuing a directive to either obtain diagnostic data or ignorediagnostic data according to said renewable power system cover status;determining the energy generated by each said renewable power systemwhich was issued a directive to obtain diagnostic data for a specifictime period to provide said renewable power system's energy generatedperformance for a given time period; determining, by at least onecomputing device, the normalized performance for each said renewablepower system, wherein the normalized performance is said renewable powersystem's energy generated performance for a given time period multipliedby said system coefficient; saving said normalized performance in saidat least one computing device with said corresponding weather andrenewable power system cover status; wherein said at least one computingdevice determines comparative information for a predetermined geographicarea based upon at least two of said diagnostic variables, and at leasttwo of said normalized performances to provide a comparative value; andinforming said consumer of said comparative value in the event of anunderperforming comparative value.
 14. A computer processor implementedmethod as in claim 13, wherein said corresponding renewable power systemcover status is selected from the group consisting of covered, partiallycovered, uncovered and indeterminate.
 15. A computer processorimplemented method as in claim 13, wherein said weather and temperatureare obtained from a third party internet weather feed source and groupedby zip code and saved in said at least one computing device.
 16. Acomputer processor implemented method as in claim 13, wherein saidweather is obtained from a third party internet weather feed source andsaid temperature is obtained by a temperature probe attached to saidrenewable power system and said weather and temperature are grouped byzip code and saved in said at least one computing device.
 17. A computerprocessor implemented method as in claim 13, further comprising the stepof: determining a geographic average of at least two renewable powersystems; determining the energy generated by each said renewable powersystem for a specific time period and saving said energy generated insaid at least one computing device along with said correspondingrenewable power system cover status
 18. A computer processor implementedmethod as in claim 13, further comprising the step of: providing athreshold time period and determining the energy generated by each saidrenewable power system for said threshold time period; sending an alertto said consumer according to said energy generated by each saidrenewable power system for said threshold time period.
 19. A method asin claim 13, wherein said step of determining comparative informationfor a predetermined geographic area based upon at least two of saiddiagnostic variables, and said normalized performance to provide acomparative value is further comprising the step of: randomly selectinga predetermined number of renewable power systems in said predeterminedgeographic area; determining the normalized performance of each saidrandomly selected renewable power system, summing said normalizedperformances to provide a sum and dividing said sum by saidpredetermined number to provide a geographic average performance; andsaving said geographic average performance in said computing device. 20.A method as in claim 13, further comprising the step of: alerting atleast one consumer when said at least one renewable power systemsperformance is a predetermined amount lower than said geographic averageperformance.
 21. A method as in claim 13, further comprising the stepsof: determining if said at least one renewable power system can beremotely adjusted; and remotely making a change to said at least onerenewable power system.